Automatic discrepancy detection in medical data

ABSTRACT

Automatic discrepancy detection in medical data is provided. In various embodiments, a disease label for a present study is determined, indicative of a disease condition. Retrospective review of a plurality of electronic medical records is performed. The retrospective review comprises searching for electronic medical records relevant to the disease condition. The earliest electronic medical record reflecting the disease condition is identified. Based on the earliest electronic medical record reflecting the disease condition, one or more of the electronic medical records having an omission or inconsistency is identified. The one or more of the electronic medical records having an omission or inconsistency are flagged for supplemental review in a worklist.

BACKGROUND

Embodiments of the present disclosure relate to discrepancy detection,and more specifically, to automatic discrepancy detection in medicaldata.

BRIEF SUMMARY

According to embodiments of the present disclosure, methods of andcomputer program products for discrepancy detection in medical data areprovided. A disease label for a present study is determined, indicativeof a disease condition. Retrospective review of a plurality ofelectronic medical records is performed. The retrospective reviewcomprises searching for electronic medical records relevant to thedisease condition. The earliest electronic medical record reflecting thedisease condition is identified. Based on the earliest electronicmedical record reflecting the disease condition, one or more of theelectronic medical records having an omission or inconsistency isidentified. The one or more of the electronic medical records having anomission or inconsistency are flagged for supplemental review in aworklist.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a system according to various embodiments of thepresent disclosure,

FIG. 2A is an exemplary echocardiogram report.

FIG. 2B is an exemplary echocardiogram study.

FIG. 3A is an exemplary image of an echocardiography screen having aplurality of measurements.

FIG. 3B illustrates exemplary templates according to embodiments of thepresent disclosure.

FIG. 3C contains exemplary output from an extraction method according toembodiments of the present disclosure.

FIG. 4 illustrates a template learning phase according to embodiments ofthe present disclosure.

FIG. 5 illustrates a set cover algorithm according to embodiments of thepresent disclosure.

FIG. 6 illustrates a template test phase according to embodiments of thepresent disclosure.

FIGS. 7A-D illustrate Doppler envelope extraction according toembodiments of the present disclosure.

FIG. 8 depicts an exemplary Picture Archiving and Communication System.

FIG. 9 illustrates an exemplary clinical image search and retrievalmethod.

FIG. 10 illustrates a method of disease detection from multimodal dataaccording to embodiments of the present disclosure.

FIG. 11 illustrates a method of detection of disease from textualdescriptions according to embodiments of the present disclosure.

FIG. 12 illustrates a method of extracting measurements from medicalimagery according to embodiments of the present disclosure.

FIG. 13 illustrates a method of extraction of measurements from Dopplerwaveforms according to embodiments of the present disclosure.

FIG. 14 illustrates a method of automatic Doppler measurement accordingto embodiments of the present disclosure.

FIG. 15 illustrates a method of discrepancy detection in medical dataaccording to embodiments of the present disclosure.

FIG. 16 depicts a computing node according to an embodiment of thepresent invention.

DETAILED DESCRIPTION

With the growth of big data through large electronic health records(EHR), there is an opportunity to leverage medical image analysis incombination with other modality data in EHR to impact the quality ofcare to patients in a significant way.

Despite the opportunity to record the data in digital electronicrecords, data entry human errors are quite common in workflow-basedelectronic health record systems. A billing operator may fail to capturea relevant billing code for a disease by looking at the problem list inan EMR system. A clinician or nurse assistant may fail to put thedisease in the problem list of the EMR system by transcribing from theexam's clinical reports. A data entry operator in an imaging facilitymay fail to transcribe all measurements from the technician into thereport. A technician may fail to capture the measurements in thediagnostic study either because he/she was not told to look for theevidence for a disease (possibly because the ordering clinician did notspecify this), or because he/she simply forgot to save the measurementsor screens on an imaging study in PACS. Any of these errors canultimately result in a patient not being flagged for a disease. If thedisease is one in which sudden death is possible, such as in aorticstenosis, catching such data entry errors through a systematic peerreview process can save lives. The present disclosure provides systemsand methods for systematically identifying such discrepancies in orderto notify the corresponding data entry operator. In various embodiments,this is done by detecting evidence for a disease from multipleinformation sources, and may include spotting mentions of the diseasename in reports, noting the measurements already made by operators thatwere not flagged, or making new measurements directly from imagingstudies.

Accordingly, the present disclosure provides systems and methods fordisease detection based on various pre-existing patient data drawn froma variety of different data sources. In particular, the presentdisclosure provides for the use of medical image analysis in combinationwith textual and other multimodal data analysis for purposes ofidentifying patient cohorts at risk for serious diseases such as aorticstenosis.

A Picture Archiving and Communication System (PACS) is a medical imagingsystem that provides storage and access to images from multiplemodalities. In many healthcare environments, electronic images andreports are transmitted digitally via PACS, thus eliminating the need tomanually file, retrieve, or transport film jackets. A standard formatfor PACS image storage and transfer is DICOM (Digital Imaging andCommunications in Medicine). Non-image data, such as scanned documents,may be incorporated using various standard formats such as PDF (PortableDocument Format) encapsulated in DICOM.

An electronic health record (EHR), or electronic medical record (EMR),may refer to the systematized collection of patient and populationelectronically-stored health information in a digital format. Theserecords can be shared across different health care settings and mayextend beyond the information available in a PACS discussed above.Records may be shared through network-connected, enterprise-wideinformation systems or other information networks and exchanges. EHRsmay include a range of data, including demographics, medical history,medication and allergies, immunization status, laboratory test results,radiology reports, radiology images, vital signs, personal statisticslike age and weight, and billing information.

EHR systems may be designed to store data and capture the state of apatient across time. In this way, the need to track down a patient'sprevious paper medical records is eliminated. In addition, an EHR systemmay assist in ensuring that data is accurate and legible. It may reducerisk of data replication as the data is centralized. Due to the digitalinformation being searchable, EMRs may be more effective when extractingmedical data for the examination of possible trends and long termchanges in a patient. Population-based studies of medical records mayalso be facilitated by the widespread adoption of EHRs and EMRs.

Health Level-7 or HL7 refers to a set of international standards fortransfer of clinical and administrative data between softwareapplications used by various healthcare providers. These standards focuson the application layer, which is layer 7 in the OSI model. Hospitalsand other healthcare provider organizations may have many differentcomputer systems used for everything from billing records to patienttracking. Ideally, all of these systems may communicate with each otherwhen they receive new information or when they wish to retrieveinformation, but adoption of such approaches is not widespread. Thesedata standards are meant to allow healthcare organizations to easilyshare clinical information. This ability to exchange information mayhelp to minimize variability in medical care and the tendency formedical care to be geographically isolated.

In various systems, connections between a PACS, Electronic MedicalRecord (EMR), Hospital Information System (HIS), Radiology InformationSystem (RIS), or report repository are provided. In this way, recordsand reports form the EMR may be ingested for analysis. For example, inaddition to ingesting and storing HL7 orders and results messages, ADTmessages may be used, or an EMR, RIS, or report repository may bequeried directly via product specific mechanisms. Such mechanismsinclude Fast Health Interoperability Resources (FHIR) for relevantclinical information. Clinical data may also be obtained via receipt ofvarious HL7 CDA documents such as a Continuity of Care Document (CCD).Various additional proprietary or site-customized query methods may alsobe employed in addition to the standard methods.

According to various embodiments of the present disclosure, automatedsystems and methods for retrospectively predicting patients likely tohave disease conditions such as aortic stenosis are provided. In variousembodiments, medical image analysis of Doppler patterns is combined withtextual content analysis of imaging and reports in a multimodal learningframework. Specifically, evidence of disease conditions such as aorticstenosis may be extracted from sources, such as billable diagnosis,significant problems from EHR, echocardiogram reports, measurementsshown on echocardiography video frames, or CW Doppler patterns inechocardiography videos. In some embodiments, disease concepts areidentified in echocardiogram reports using a concept extractionalgorithm to detect UMLS concept vocabularies and their relevantassociated measurements. In some embodiments, measurements captured byechocardiogaphers are reliably extracted through selective imageprocessing and optical character recognition in tabular regions onechocardiogram video frames. In some embodiments, diagnosticallyrelevant measurements for aortic stenosis are automatically extractedfrom Doppler envelopes using a three step process of relevant Dopplerframe identification, envelope tracing and measurement extraction. Theframe identification includes classification using convolutional neuralnetwork (CNN)-based learned features from Doppler regions. The envelopextraction is made robust by incorporating echocardiographer tracings.In some embodiments, the disease-specific features extracted from eachmultimodal source of information are combined using a random forestlearning formulation to predict patients that are likely to have aorticvalve disease.

Referring to FIG. 1, an exemplary system according to embodiments of thepresent disclosure is illustrated. A variety of data sources 101 . . .104 contain various patient information. It will be appreciated that thenumber of data sources is purely exemplary, and that patient informationmay be spread over an arbitrary number of data sources, connected, forexample, via a LAN, WAN, or the interne. Patient data stored in datasource 101 . . . 104 may include imaging studies 105, textural report106, lab data 107, problem list 108, or diagnosis list 109. In someembodiments, imaging studies 105 or other data are located in a PACS. Insome embodiments, problem list 108 or other data are located in an EHRsystem. In various embodiments, imaging studies 105 are subject tomeasurement or data extraction 110 using various methods set out belowto extract data contained in the imagery. In various embodiments,textual reports 106 are subject to concept extraction 111 using variousmethods set out below to extract clinical concepts. Data resulting fromdata extraction 110, concept extraction 111, and additional patient data107 . . . 109 is supplied to a classifier 112. Based on the inputfeatures, classifier 112 provides a disease label 113.

The present disclosure provides for identifying patients at risk ofvarious disease conditions such as aortic stenosis by combining medicaltext and image analysis of medical data such as echocardiagram studies.Such multimodal information may be used for cohort identification.Several embodiments provided herein overlap three inter-disciplinaryfields of text analysis, optical character recognition (OCR), andmedical image analysis. In various embodiments extraction of clinicalconcepts from text is provided. In various embodiments, measurements areextracted in addition to disease name mentions for detection of diseaseconditions. In various embodiments, OCR extraction of clinicalmeasurements from text screens of studies such as echocardiogram studiesis provided. Some such embodiments do not rely on manual creation oftemplates for various manufacturer's echo screens. In variousembodiments, reliable extraction of Doppler envelopes is provided, evenin the presence of electrocardiogram (ECG) fluctuations duringarrhythmia and overlay artifacts in Doppler spectra. In variousembodiments, the automatic selection of Doppler frames depicting aorticvalves is provided.

Various exemplary embodiments provided herein are given with regard toaortic stenosis. However, it will be apparent that the presentdisclosure is suitable for application to any other disease conditionthat may be tied to clinical data or observations. Aortic stenosis (AS)is a common heart disease that can result in sudden death. It can bediagnosed through the Doppler patterns in echocardiogram studies such asthe exemplary study shown in FIG. 2B. Although the disease can betreated through surgery or transcatheter aortic valve replacements(AVR), it often goes untreated for several reasons. The absence of chestpain and other symptoms may make the disease asymptomatic and not acandidate for detection in echocardiographer's instructions. Thistogether with echocardiographer skill errors can cause a Doppler patterndepicting the disease to be missed entirely. For example, FIG. 2B showsone such case where the echocardiographer missed the evidence formoderate aortic stenosis in the Doppler spectrum. When the relevantmeasurements are made by the echocardiographer and inserted into thestudy screens, they may still fail to make it into the overall report.Finally, even if the pattern is detected and makes it into theechocardiogram report, such as the exemplary report in FIG. 2A, puredata entry errors in EHR can leave out the evidence of the disease froma patient record. With thousands of echocardiography studies takenannually, manual peer review is costly and rarely performed, with theresult that many patients are going untreated.

Disease Extraction from Reports

In various embodiments of the present disclosure, extraction of evidenceof a disease condition from clinical reports is provided. In someexemplary embodiments, the disease condition is aortic stenosis and theclinical report is an echocardiogram report.

To extract evidence of aortic stenosis from echocardiogram reports, alarge knowledge graph is generated of over 5.6 million concept terms bycombining over 70 reference vocabularies such as SNOMED CT, ICD9, ICD10,RadLex, RxNorm, and LOINC, where its concept nodes are used asvocabulary phrases. The detections of clinical concepts within sentencesof the clinical reports uses the longest common subfix (LCF) algorithm.To detect evidence of stenosis, tuples of (D_(i), S_(j), A_(k), V_(l)),are found where D_(i) are disease name indicators (e.g., “aortic valvedisorders,” “aortic valve stenosis,” etc.), S_(j) are specific symptomsassociated with the disease such as “chest pain,” A_(k) are anatomicalabnormalities such as “thickened,” “calcified,” and V_(l) are qualifierssuch as “mild,” “moderate,” “severe.” These detections are done withinneighboring sentences in selected paragraphs where the aortic valve isdescribed in echocardiogram reports.

Next, key measurement names are selected indicating aortic stenosis,such as peak velocity, mean pressure gradient, and aortic valve area.Using their values ranges and units, a measurement name-value pairdetector is developed. As the spoken utterances of these names vary inechocardiograms, n-gram analysis is performed of a corpus of over 50,000reports in a data collection to identify all such significant variantsof the measurement names. To detect occurrences of measurement names andtheir associated values within the context of a detected sentence, thepattern of their occurrences in a sentence is analyzed usingpart-of-speech (POS) tagging, and dependency graph parsing. For eachroot concept (e.g., ‘gradient’), a chain of its modifiers (in the formof nouns or adjectives, e.g., ‘mean trans aortic’) are automaticallyidentified from a sentence using an automatic POS tagger. In someembodiments, the automatic POS tagger comprises the Stanford POS tagger.By analyzing thousands of sentences containing the occurrences ofmeasurement vocabulary terms in connection with measurement values andunits, regular expression patterns are formed, such a pattern

A

B

C

where A is any disease indicating phrase A: {aorta, aortic, AV, AS}, Bis any measurement term {gradient, velocity, area}, and C is no negationterms of the kind {no, not, without, neither, none}. Once the pattern ismatched, numeric values are located following the measurement names inthe same sentence that were juxtaposed with names of relevant units. Anexample of aortic stenosis measurement extraction is illustrated in theparagraph below.

“Aortic Valve: The aortic valve is thickened and calcified. Severeaortic stenosis is present. The aortic valve peak velocity is 6.18 m/s,the peak gradient is 152.8 mmHg, and the mean gradient is 84.9 mmHg. Theaortic valve area is estimated to be 0.28 cm².”

In general, the text-based aortic stenosis detection is fairly stablewith very few false positives as indicated in Table 1. Only 3 errorswere observed among thousands of patients after a thorough analysis ofthe detected cases, as listed in the third column. Table 1 illustratesthe false discovery rate (FDR) of disease (AS) and measurement (peakvelocity and mean gradient) detection.

TABLE 1 FDR False Positives AS 2/191 Indication/Hx: EVAL FOR MS/MR,AS/AI De-Identied AS SMOKER Peak Velocity 1/364 aortic stenosis ispresent. The aortic valve peak velocity is 2.6 

 9 m/s, the peak gradient is 28.9 mmHg Mean Gradient 0/410 —

Extracting Echocardiographer Measurements

Referring to FIG. 3, an illustration is provided of measurementextraction from echocardiography screens.

The evidence for aortic stenosis can be extracted from the measurementsmade by the echocardiographer captured as text-only screens such as theone shown in FIG. 3A. To extract the measurements, the frames depictingthe measurements are selected. A relevant tabular template is applied toidentify the semantic names of the measurements. An exemplary tabulartemplate is shown in FIG. 3B. Section template 301 includes section name302 and one or more OCR boxes 303 . . . 306 having a relative offsetbetween then. Measurement template 307 includes a measurement name 308and one or more value templates 309 . . . 310. Each value template 309 .. . 310 includes a section reference 311 (e.g., to the section oftemplate 301) and one or more OCR boxes 312 . . . 314 having a relativeoffset between then.

An optical character recognition algorithm is used to extract text. Insome embodiment, the DataCap OCR engine is used while in otherembodiments, the Tesseract OCR engine is used. However, it will beappreciated that any number of OCR engines are suitable for useaccording to the present disclosure. The document layout templates ofdevice manufacturer's screens is learned automatically. The templatelearning is focused per anatomical region and exploits the invariance intopological layout of the measurement name value pairs in the tabularregions. Once the templates are learned, they are matched to any giventext only screen to read off the expected measurement names.

The images are processed within the text regions through an imageenhancement process to increase the robustness of OCR. FIG. 3C shows thetext extracted from measurement screen of FIG. 3A using this video textdetection algorithm. The OCR-based measurement extraction module wastested on 114 text-only frames across 114 patients, and a total of 1719measurements were verified. For this validation set, the systemextracted 99.7% of the measurements correctly, with the remaining errorscaused by the numeric values being split by the OCR engine.

Referring to FIG. 4, a learning phase for template generation isillustrated according to embodiments of the present disclosure. A sampleimage collection 401, comprising a plurality of images is read. Theimages are ranked 402 according to the set cover algorithm set forthbelow. Supersets 403 and subsets 404 of images are identified to avoidduplication and thus make annotation faster. A GUI 405 is provided thatdisplays automatic attribute-value pair suggestions based on arule-based approach and allows input of user corrections. Based on thecorrected attribute-value pairs and their layout, a template isgenerated 406.

According to various embodiments, the training mode takes in an initialsample of images drawn from a larger corpus of medical imagery. In someembodiments, this initial sample comprises ˜100-200 randomly selectedimages. In addition to generating a template as set forth herein, a listof common typos is also compiled based on the OCR module output. Thislist is useful to supplement the knowledge base used to increasesmeasurement accuracy. In order to simplify and speed up training, thetraining images are ranked for presentation to a user for verification,as discussed above. The ranking algorithm allows presentation of imageswith the most information to the user in the earliest stages oftraining. In this way, the initial determinations made by the system arereinforced. Via a user interface, a user can correct for mistakes ormissed measurements. By optimizing presentation of screens forverification, a user is required to evaluate fewer images and to spendless time overall on validation and correction.

As noted above, in various embodiments, a graphical user interface isprovided for training. Given a pool of new machine images N, they areclustered into k different groups G. The images are ranked based on theset cover algorithm given below. A user is presented with one or moreimages from each group G in combination with an initial determination,which in some embodiments is based on rule-based approach, for everymeasurement attribute-value-unit pair and its section header. In someembodiments, the rule based approach comprises applying predeterminedtemplates based on vendor and device information associated with theimage. For example, a given vendor may always place certain measurementsat a predetermined location in a frame, while other vendors may havesignificant variation among product lines. Such a rule based approach isused to detect table structure to locate section names, table header,measurement names and units.

Through the UI, a user can annotate the image, i.e., by correctingmisclassified pairs, removing non-relevant words, or adding user-definednaming. Subsequent images may then be updated based on the currentcorrections. This may reduce by half the amount of clicking in followingannotations.

The above process may be repeated until all images in the training sethave been annotated. Image templates of each group G_(i):t_(i), . . .,t_(j) are merged to generate a cluster-specific/rank-specific templateT_(i). After training, the machine-specific template comprises a set ofcluster-templates/rank-templates, i.e., T

{T₁, . . . ,T_(k)}, which can be used to extract measurements from anytest image at run time.

Referring to FIG. 5, a set cover algorithm is illustrated according toembodiments of the present disclosure. The set cover algorithmidentifies a set of images (superset) that can represent all of theimages (superset+subset). In each step of this greedy approach, an imageis chosen to the superset that has the maximum profit as given byEquation 1, where I_(i) is the indicator of word i and f_(i) is thenormalized frequency of word i. A plurality of images 501 . . . 503 areanalyzed, each containing a plurality of words. 504. At each iterationof the algorithm, an image is picked that has maximum p. The algorithmconcludes when all words are covered, yielding superset and subsets 505.

$\begin{matrix}{{p = {\sum\limits_{i}{I_{i}f_{i}}}},} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Referring to FIG. 6, a measurement extraction and testing method isillustrated according to embodiments of the present disclosure. Inruntime mode, systems of the present disclosure extract measurementsautomatically using the knowledge and the templates. Extracted text 601and original images 602 are provided to user interface 603 for display.Based on the template, a list of measurements 604 is extracted. Imagescontaining regions or combinations of measurements not reflected in thetemplate 605 may be flagged and used for further training of the system.

In the runtime stage, a new image is assigned to a cluster based on asimilarity score. The cluster-template is retrieved and used to extractmeasurements from the test image. If there is no close template, thedefault rule-based approach is used to extract the measurements. T issearched extensively to see if any measurements are matched to the testimage. Images that need retraining will be set aside or flagged so usercan rerun the training phase to create a new template from theunclassified test image.

Disease Extraction from Doppler Image Analysis

In Doppler echocardiography images, the clinically relevant region isknown to be within the Doppler spectrum, contained in a rectangularregion of interest as shown in FIG. 2B. To ensure the measurementextraction is attempted on relevant frames depicting the aortic valve, aclassifier is provided using features derived from the region depictingDoppler patterns in images. This image region is fed to a pre-trainedconvolutional neural network (CNN) consisting of 5 convolution layers,two fully connected layers and a SoftMax layer with 1000 output nodes.The CNN is used as a feature generator here. Even though the CNN wastrained in another imaging domain, the earlier layers of the neuralnetwork capture generic features such as edges which are also applicablein the present domain. For feature generation, a feature vector of size4096 is harvested at the output of the first fully connected layer ofthe network and the images classified using a support vector machine(SVM) classifier. To train the SVM, an expert reviewed dataset of 496 CWDoppler patterns is used, each labeled with one of the four valve types.A set consisting of 100 of these images was randomly isolated as a testset. The SVM was optimized for kernel type and slack and kernelvariables on the remaining 396 images using five-fold cross validation.Using the CNN derived features, the SVM achieved an accuracy of 92%across all valves with all aortic valve CW Doppler frames being labeledcorrectly. The tricuspid stenosis valve pattern accounted for nearlyhalf the errors as it is similar to the aortic stenosis valve pattern.

In various embodiments, a frame of interest is read, and the modereflected in the frame is determined in advance of valve detection. Forexample, a frame may reflect B-Mode, M-Mode, CW-Doppler, PW-Doppler,Text-Panel, Color-Doppler, Color-Doppler M-Mode. An frame, or a regionof interest therein is provided to a pre-trained convolutional neuralnetwork (CNN). The CNN is used as a feature generator. In someembodiments, a feature vector of size 4096 is harvested at the output ofthe first fully connected layer of the network and the images classifiedusing a support vector machine (SVM) classifier. To train the SVM, adataset of imagery is used reflecting a plurality of modes, each labeledwith the appropriate mode. In embodiments including mode detection, thevalve detection stage may take as input the mode label. In someembodiments, multiple valve detection stages are maintained,corresponding to each possible mode label, and the image is routed tothe appropriate valve detection stage according to the valve label.

Extraction of Doppler Patterns

Extracting of Doppler spectrum in some embodiments uses pre-processingsteps of region of interest detection, ECG extraction, and periodicitydetection. In addition, various embodiments exploit the tracings ofechocardiographers as shown in FIG. 7. To extract echocardiographer'senvelope annotation, the calculated Doppler velocity profile 701 isexcluded from the ROI. Otsu's thresholding algorithm is applied on theremaining image (as pictured in FIG. 7B) to highlight the manualdelineation 702 which is connected to the baseline. Then, the extractedannotation (shown in FIG. 7C) is added to the filled up largest region(from FIG. 7B). The boundary pixels are traced as shown in FIG. 7D. TheDoppler envelop extraction was tested on over 7000 images duringtraining, and the results of the various stages of processing areindicated in Table 2.

TABLE 2 Measurement made Images tested Error V_(max) 1054 0.29 ± 0.78m/sec M_(g) 785 0.08 ± 10.05 mmHg

Measurement Extraction from Doppler Patterns

Maximum jet velocity (V_(max)) is defined as the peak velocity in thenegative direction for the Doppler pattern for aortic stenosis. Sincethe Doppler envelope traces are available, the pixel value of thenegative peak in the Doppler spectra can be easily noted. To convert theimaging-based measurement to a physical velocity value, the textcalibration markers on the vertical axis in the ROI are analyzed usingan OCR engine to read off the velocity value. The maximum value ofvelocity during systole within each cycle is a candidate for theV_(max). The second measurement indicative of aortic stenosis is meanpressure gradient (MPG). MPG is calculated from velocity informationfollowing the estimation in Equation 2, where N is the number of pixelswithin the QT interval of ECG, and V is the velocity.

$\begin{matrix}{M_{g} \approx {\sum\limits_{V}\frac{4V^{2}}{N}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

Disease Prediction Using Multimodal Learning

In some embodiments, after collecting all the measurements derived fromeach modality processing, a feature vector is formed as follows inEquation 3, where the ‘b’ is for billable diagnosis, ‘s’ for significantproblems, ‘t’ for textual reports, ‘o’ for video text, and ‘i’ for imageanalysis features. The first 3 features are binary while the rest areactual measurements made in the respective modalities. To train thepredictor, a set of patients may be used with known aortic stenosis(independently validated clinically), and learn the correlation betweenfeature values and the disease label (aortic stenosis) using a randomforests learner. In some embodiments, the random forests are constructedwith T trees (e.g., T=4, T=100), with each tree having a minimum nodesize n (e.g., n=4, n=10), and maximum depth of 10. It will beappreciated that the parameters can be tuned depending on the task ofinterest and the size of the available training data.

F _(p) ={V _(1b) ,V _(2s) ,V _(3t) ,V _(4t) , V _(5t) , V _(6o) ,V _(7o),V _(8i) ,V _(9i)}  Equation 3

Given determination of a disease label as described above, prior studiesmay be surveyed to determine when such a diagnosis first occurred. Inthis way, gaps or discrepancies in the record may be located. When a gapor discrepancy is detected, a notification may be dispatched to atechnician or other personnel such as a medical coder.

According to various embodiments, when reading a current study for apatient the past study records for that patient are processed (bothreports NLP and image cognition) and compared to the current study bothas prior positive findings to reconfirm and as incidental findings(e.g., a mass was seen but dismissed as benign). In some embodiments,prior inconsistent results may be included in a PACS worklist. In thismanner, a re-read or review process may be triggered.

A retrospective clinical study was conducted on a large patient data setacquired from a nearby hospital. The experimental context was toevaluate if there were missed diagnosis of aortic stenosis in theirrecords when in fact evidence could be found from the underlyingclinical data. Specifically, the analysis was restricted to patients forwhich 4 modalities of information were available, namely, billablediagnosis, significant problems, and echocardiogram reports and imagingstudies giving rise to a total of 991 patients with 1,226 reports and121,811 Doppler images. These studies were independently validatedclinically and 395 patients were found to have aortic stenosis servingas the ground truth.

A 10 fold cross-validation was done by randomly splitting the data into10 folds, 9 for training and 1 for testing. Table 3 shows the precision,recall, F-score, and overall accuracy of the baseline and random forestswith different combinations of features, including a fusion of image andOCR features—referred to as min(1, 0). Selecting the minimum of thesetwo values gave a more conservative estimate of the severity of thedisease. Out of the 395 patients manually identified by experts, 99 werenewly discovered patients from our multimodal analysis giving rise toover 25% new discoveries.

Table 3 illustrates comparative performance of rule-based baseline andrandom forest with features extracted from structured information,reports, images, and OCR text. min(1, 0) refers to the fusion of imageand OCR features by taking the minimum of the two for each individualfeature/parameter.

TABLE 3 Features Performance Structured Report Image OCR min(I, O)Precision Recall F-score Accuracy Baseline x x x x 0.84 1.00 0.93 0.92Random x 1.00 0.53 0.70 0.81 Forest x 0.96 0.55 0.70 0.81 x 0.80 0.500.62 0.75 x 0.94 0.50 0.66 0.79 x 0.94 0.56 0.70 0.81 x x 0.78 0.59 0.670.77 x x 0.93 0.73 0.82 0.87 x x x 0.82 0.71 0.77 0.83 x x x 0.96 0.890.92 0.94 x x x x 0.87 0.89 0.88 0.90

Comparison Against Baseline

The baseline was a rule-based model, which returned all patients with atleast one piece of evidence from any of five sources. Here the evidencewas either the presence of disease mentions or exceeding the normalranges for V_(max) and M_(g) according to the AHA guidelines. Thebest-performing model was a random forest with features from all thedifferent sources, achieving 96% precision that is 12% higher than thebaseline. Combining features using random forests compensates forpotential errors in individual modality detections, making its precisionhigher than the baseline method. The higher precision will reduceunnecessarily flagging of patients which would have otherwise havelowered the confidence in such prediction system for practical uses.

Referring to FIG. 8, an exemplary PACS 800 consists of four majorcomponents. Various imaging modalities 801 . . . 809 such as computedtomography (CT) 801, magnetic resonance imaging (MRI) 802, or ultrasound(US) 803 provide imagery to the system. In some implementations, imageryis transmitted to a PACS Gateway 811, before being stored in archive812. Archive 812 provides for the storage and retrieval of images andreports. Workstations 821 . . . 829 provide for interpreting andreviewing images in archive 812. In some embodiments, a secured networkis used for the transmission of patient information between thecomponents of the system. In some embodiments, workstations 821 . . .829 may be web-based viewers. PACS delivers timely and efficient accessto images, interpretations, and related data, eliminating the drawbacksof traditional film-based image retrieval, distribution, and display.

A PACS may handle images from various medical imaging instruments, suchas X-ray plain film (PF), ultrasound (US), magnetic resonance (MR),Nuclear Medicine imaging, positron emission tomography (PET), computedtomography (CT), endoscopy (ES), mammograms (MG), digital radiography(DR), computed radiography (CR), Histopathology, or ophthalmology.However, a PACS is not limited to a predetermined list of images, andsupports clinical areas beyond conventional sources of imaging such asradiology, cardiology, oncology, or gastroenterology.

Different users may have a different view into the overall PACS system.For example, while a radiologist may typically access a viewing station,a technologist may typically access a QA workstation.

In some implementations, the PACS Gateway 811 comprises a qualityassurance (QA) workstation. The QA workstation provides a checkpoint tomake sure patient demographics are correct as well as other importantattributes of a study. If the study information is correct the imagesare passed to the archive 812 for storage. The central storage device,archive 812, stores images and in some implementations, reports,measurements and other information that resides with the images.

Once images are stored to archive 812, they may be accessed from readingworkstations 821 . . . 829. The reading workstation is where aradiologist reviews the patient's study and formulates their diagnosis.In some implementations, a reporting package is tied to the readingworkstation to assist the radiologist with dictating a final report. Avariety of reporting systems may be integrated with the PACS, includingthose that rely upon traditional dictation. In some implementations, CDor DVD authoring software is included in workstations 821 . . . 829 toburn patient studies for distribution to patients or referringphysicians.

In some implementations, a PACS includes web-based interfaces forworkstations 821 . . . 829. Such web interfaces may be accessed via theinternet or a Wide Area Network (WAN). In some implementations,connection security is provided by a VPN (Virtual Private Network) orSSL (Secure Sockets Layer). The clients side software may compriseActiveX, JavaScript, or a Java Applet. PACS clients may also be fullapplications which utilize the full resources of the computer they areexecuting on outside of the web environment.

Communication within PACS is generally provided via Digital Imaging andCommunications in Medicine (DICOM). DICOM provides a standard forhandling, storing, printing, and transmitting information in medicalimaging. It includes a file format definition and a networkcommunications protocol. The communication protocol is an applicationprotocol that uses TCP/IP to communicate between systems. DICOM filescan be exchanged between two entities that are capable of receivingimage and patient data in DICOM format.

DICOM groups information into data sets. For example, a file containinga particular image, generally contains a patient ID within the file, sothat the image can never be separated from this information by mistake.A DICOM data object consists of a number of attributes, including itemssuch as name and patient ID, as well as a special attribute containingthe image pixel data. Thus, the main object has no header as such, butinstead comprises a list of attributes, including the pixel data. ADICOM object containing pixel data may correspond to a single image, ormay contain multiple frames, allowing storage of cine loops or othermulti-frame data. DICOM supports three- or four-dimensional dataencapsulated in a single DICOM object. Pixel data may be compressedusing a variety of standards, including JPEG, Lossless JPEG, JPEG 2000,and Run-length encoding (RLE). LZW (zip) compression may be used for thewhole data set or just the pixel data.

Referring to FIG. 9, an exemplary PACS image search and retrieval method900 is depicted. Communication with a PACS server, such as archive 812,is done through DICOM messages that that contain attributes tailored toeach request. At 901, a client, such as workstation 821, establishes anetwork connection to a PACS server. At 902, the client prepares a DICOMmessage, which may be a C-FIND, C-MOVE, C-GET, or C-STORE request. At903, the client fills in the DICOM message with the keys that should bematched. For example, to search by patient ID, a patient ID attribute isincluded. At 904, the client creates empty attributes for all the valuesthat are being requested from the server. For example, if the client isrequesting an image ID suitable for future retrieval of an image, itinclude an empty attribute for an image ID in the message. At 905, theclient send the message to the server. At 906, the server sends back tothe client a list of one or more response messages, each of whichincludes a list of DICOM attributes, populated with values for eachmatch.

Referring to FIG. 10, a method of disease detection from multimodal datais illustrated according to embodiments of the present disclosure. At1001, a plurality of patient records associated with a patient are readfrom a plurality of data sources. At 1002, a plurality ofdisease-specific features are extracted from the plurality of patientrecords. At 1003, the plurality of disease-specific features areprovided to a classifier. At 1004, an indicator of a likely diseasecondition of the patient is received from the classifier.

Referring to FIG. 11, a method of detection of disease from textualdescriptions is illustrated according to embodiments of the presentdisclosure. A knowledge graph is used to detect clinically meaningfulconcepts. In various embodiments, the knowledge graph models a pluralityof associations between disease names, symptoms, anatomicalabnormalities, and qualifiers based on phrase vocabularies from UMLS andcustom vocabulary sources. At 1101, a knowledge graph of clinicalconcepts is read. At 1102, based on the knowledge graph, a plurality ofassociations are determined between disease names, symptoms, anatomicalabnormalities, and qualifiers. At 1103, a corpus of clinical reports isread. At 1104, based on the plurality of associations, a plurality ofportions indicative of a disease condition are located within the corpusof clinical reports. At 1105, name/value pairs are detected within eachof the plurality of portions corresponding to measurements indicative ofthe disease condition. The disease condition is also captured in theknowledge graph. At 1106, the measurements indicative of the diseasecondition are extracted.

Referring to FIG. 12, a method of extracting measurements from medicalimagery is illustrated according to embodiments of the presentdisclosure. The text layout regions within medical images are clusteredto learn tabular layout of text. At 1201, a first medical imagecontaining embedded text is read. At 1202, a plurality of measurementnames are read. At 1203, optical character recognition is applied to thefirst medical image to locate the plurality of measurement names withinthe medical image. At 1204, from the locations of the plurality ofmeasurement names within the first medical image, a tabular template isgenerated indicative of a layout of measurement name/value pairs.Disease-specific measurement names are captured in a measurementvocabulary and indexed by the target disease. The candidatemeasurement-value pairs may then be stored as disease-indicatingfeatures.

Referring to FIG. 13, a method of extraction of measurements fromDoppler waveforms is illustrated according to embodiments of the presentdisclosure. At 1301, a frame is selected from a medical video. Theselected frame depicts a valve of interest. At 1302, a Doppler envelopeis extracted from the selected frame. At 1303, based on the frame andthe Doppler envelope, one or more measurements indicative of a diseasecondition are extracted.

Referring to FIG. 14, a method of automatic Doppler measurement isillustrated according to embodiments of the present disclosure. At 1401,a plurality of frames of a medical video are read. In some embodiments,a mode label indicative of a mode of each of the plurality of frames isdetermined. At 1402, each of the plurality of frames is provided to atrained feature generator. At 1403, a plurality of feature vectorscorresponding to the plurality of frames is obtained from the trainedfeature generator. At 1404, the plurality of feature vectors is providedto a trained classifier. At 1405, a valve label indicative of a valvecorresponding to each of the plurality of frames is obtained from thetrained classifier. At 1406, one or more measurement indicative of adisease condition is extracted from those of the plurality of framesmatching a predetermined valve label.

In various embodiments, determining the mode label includes providingthe plurality of frames to a second trained feature generator. Aplurality of feature vectors corresponding to the plurality of framesare obtained from the second trained feature generator. The plurality offeature vectors is provided to a second trained classifier. The modelabel is obtained from the second trained classifier. In suchembodiments, a set of features discriminating between mode labels arelearned by the classifier, which may be a deep learning network, using aset of prior chosen training images. These features are then extractedfrom incoming images and then classified.

In various embodiments, an incoming imaging study is processed to firstselect frames depicting CW Doppler pattern. A plurality of frames of amedical video are read. A mode label indicative of a mode of each of theplurality of frames is determined. A set of features discriminatingbetween mode labels are learned using a deep learning network using aset of prior chosen training images. These features are then extractedfrom incoming images and classified. The images classified into CWDoppler mode labels are then retained. From among the frames depictingCW Doppler patterns, a set of frames are selected that depict the valveof interest. A region of interest is extracted in each CW Doppler imageand features discriminating between different heart valves are thenlearned using another deep learning network using prior chosen trainingCW Doppler images. New CW Doppler images are classified using thelearned network and those images that are classified as containing thetarget valve of interest are retained. A Doppler envelope is thenextracted from the selected frame. Based on the frame and the Dopplerenvelope, one or more measurements is indicative is extracted indicativeof a disease condition from those of the at least one of the pluralityof frames matching a predetermined valve label.

Referring to FIG. 15, a method of discrepancy detection in medical datais illustrated according to embodiments of the present disclosure. At1501, a disease label is determined, indicative of a disease condition.In some embodiments, the disease label corresponds to a present study.At 1502, retrospective review of a plurality of electronic medicalrecords is performed. The retrospective review comprises searching forelectronic medical records relevant to the disease condition. At 1503,the earliest electronic medical record reflecting the disease conditionis identified. At 1504, based on the earliest electronic medical recordreflecting the disease condition, one or more of the electronic medicalrecords having an omission or inconsistency is identified. At 1505, theone or more of the electronic medical records having an omission orinconsistency are flagged for supplemental review in a worklist.

In various embodiments, retrospective review includes searching forelectronic medical records relevant to the disease condition. Thisincludes problem list and recorded diagnosis, clinical reports andimaging studies. Given a target disease for whom the discrepancy needsto be detected, and a time period over which the discrepancy needs to bedetected, a set of encounters are selected from the patient recordscorresponding to the specified time period. The imaging studies, andtheir associated reports are then drawn. The problem list and recordeddiagnosis associated with these encounters are drawn. The diseasemention in the problem list and recorded diagnosis is noted.Disease-specific features are extracted from imaging studies andreports. The disease specific features along with indications for thedisease from problem lists are fed to the classifier to predict patientsthat are likely to have the disease. From the list of patientsidentified by the predictor classifier, their encounter records are nowanalyzed for discrepancy. Evidence from each step in the workflow iscompared to the next step with respect to the indications for thedisease. In spotting a discrepancy the positive occurrence of diseasemention is used. Similarly in spotting a discrepancy in modality data(imaging), established guidelines for exceeding the values for normalranges of measurements are utilized. The order of analysis ofdiscrepancy in the workflow begins with the earliest evidence inimaging, then to technician measurements embedded in imaging, followedby imaging reports and then problem list and finally the recordeddiagnosis in the system. With this order, the earliest place in theelectronic medical record where this discrepancy has occurred is noted.The definition of a discrepancy is simply that the earlier stage in theworkflow says ‘yes’ to the disease while the next consecutive stage inthe workflow says ‘no’.

Referring now to FIG. 16, a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a suitable computingnode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, computing node 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 16, computer system/server 12 in computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A method comprising: determining a disease label indicative of a disease condition; performing retrospective review of a plurality of electronic medical records, the retrospective review comprising searching for electronic medical records relevant to the disease condition; identifying the earliest electronic medical record reflecting the disease condition; based on the earliest electronic medical record reflecting the disease condition, identifying one or more of the electronic medical records having an omission or inconsistency; flagging the one or more of the electronic medical records having an omission or inconsistency for supplemental review in a worklist.
 2. The method of claim 1, wherein performing retrospective review comprises searching problem lists, recorded diagnosis, clinical reports, or imaging studies.
 3. The method of claim 1, wherein performing retrospective review comprises: selecting a time period; selecting a set of encounters corresponding to the time period; retrieving a plurality of imaging studies and associated reports corresponding to the selected set of encounters; retrieving a problem list and a recorded diagnosis associated with the selected set of encounters.
 4. The method of claim 3, further comprising: locating a mention in the problem list or the recorded diagnosis corresponding to the disease label.
 5. The method of claim 3, further comprising: extracting disease-specific features from the imaging studies and associated reports; providing the disease-specific features to a trained classifier; determining from the classifier whether a patient is likely to have the disease condition.
 6. The method of claim 1, wherein the retrospective review further comprises determining disease labels for each of the plurality of electronic medical records.
 7. The method of claim 1, further comprising: alerting a treating physician of the omission or inconsistency.
 8. The method of claim 1, further comprising: providing a bookmark to each of the one or more of the electronic medical records having an omission or inconsistency.
 9. The method of claim 1, further comprising: presenting the worklist to a user.
 10. A computer program product for discrepancy detection in medical data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: determining a disease label indicative of a disease condition; performing retrospective review of a plurality of electronic medical records, the retrospective review comprising searching for electronic medical records relevant to the disease condition; identifying the earliest electronic medical record reflecting the disease condition; based on the earliest electronic medical record reflecting the disease condition, identifying one or more of the electronic medical records having an omission or inconsistency; flagging the one or more of the electronic medical records for supplemental review in a worklist.
 11. The computer program product of claim 10, wherein performing retrospective review comprises searching problem lists, recorded diagnosis, clinical reports, or imaging studies.
 12. The computer program product of claim 10, wherein performing retrospective review comprises: selecting a time period; selecting a set of encounters corresponding to the time period; retrieving a plurality of imaging studies and associated reports corresponding to the selected set of encounters; retrieving a problem list and a recorded diagnosis associated with the selected set of encounters.
 13. The computer program product of claim 12, the method further comprising: locating a mention in the problem list or the recorded diagnosis corresponding to the disease label.
 14. The computer program product of claim 12, the method further comprising: extracting disease-specific features from the imaging studies and associated reports; providing the disease-specific features to a trained classifier; determining from the classifier whether a patient is likely to have the disease condition.
 15. The computer program product of claim 9, wherein the retrospective review further comprises determining disease labels for each of the plurality of electronic medical records.
 16. The computer program product of claim 9, the method further comprising: alerting a treating physician of the omission or inconsistency.
 17. The computer program product of claim 9, the method further comprising: providing a bookmark to each of the one or more of the electronic medical records having an omission or inconsistency.
 18. The computer program product of claim 9, the method further comprising: presenting the worklist to a user.
 19. A system comprising: a datastore comprising a plurality of electronic medical records; a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising: determining a disease label indicative of a disease condition; performing retrospective review of the plurality of electronic medical records, the retrospective review comprising searching for electronic medical records relevant to the disease condition; identifying the earliest electronic medical record reflecting the disease condition; based on the earliest electronic medical record reflecting the disease condition, identifying one or more of the electronic medical records having an omission or inconsistency; flagging the one or more of the electronic medical records for supplemental review in a worklist.
 20. The system of claim 19, wherein performing retrospective review comprises: selecting a time period; selecting a set of encounters corresponding to the time period; retrieving a plurality of imaging studies and associated reports corresponding to the selected set of encounters; retrieving a problem list and a recorded diagnosis associated with the selected set of encounters. 